Leading Change

AI is showing up in every strategy deck, every board meeting, and every company roadmap. But if you look closely, most AI initiatives are quietly failing long before they ever deliver value.
In this episode of Leading Change in the Wild, I sit down with Brandy Ferrer to break down the real reasons AI projects fall apart and what leaders can do to keep their investments on track.
📉 Here’s what we unpack:
Why “AI first” mindsets push teams to chase hype instead of solving real problems
How unclear goals and poor communication derail even the best technical solutions
The role culture plays in whether employees adopt or reject new AI tools
Why companies overestimate the technology and underestimate the human side
What leaders can do to design AI initiatives that actually stick
AI isn’t magical. It isn’t plug-and-play. And it isn’t a shortcut.It’s a tool that only works when we prepare our organizations to use it well.
👇 Let’s discuss:Where do you see AI initiatives breaking down in your industry?What’s one thing leaders should focus on before implementing AI?

Is synthetic data the solution to "jagged" enterprise AI... or the fast track to Model Collapse?We just got used to "Agentic AI." Now, Salesforce is defining the next frontier of automation with the new term Enterprise Generalized Intelligence (EGI) and betting big on synthetic data to train its new Agent Force solutions. But is this the right path for enterprise trust?In this episode of Leading Change of the Wild, I dig into Salesforce's move and the massive risks involved in training AI on "fake" data.Here’s what I explore:
What Salesforce's new term (EGI) really means and why they introduced it.
The argument for synthetic data: cost savings, compliance (HIPAA), and mitigating historical bias.
The critical risk of Model Collapse when AI models are trained on their own generative outputs.
When synthetic data makes sense (e.g., self-driving cars and fraud detection) versus general enterprise use.
The paradox: Using synthetic data to smooth out models may introduce new, unverified bias and hurt trust.
The goal is 100% accurate, trustworthy AI. But training models on data that was literally designed to mimic human output might be the opposite of what's needed for lasting organizational trust.👇 Let’s discuss:Do you believe synthetic data is a viable path to increasing AI trust and accuracy in the enterprise?Should models be honed with proprietary data or a specialized synthetic environment before deployment?

AI is fueling record-breaking valuations, trillion-dollar companies, and endless hype. But… are we living in a bubble?
In this episode of Leading Change in the Wild, I dig into what’s really happening behind the scenes of the AI boom and what it could mean for leaders and organizations betting big on this technology. 📉
Here’s what I unpack:
Why Nvidia’s $5 trillion valuation has leaders divided
What Bill Gates and Sam Altman are saying about overhype and dead-end investments
How “circular funding” between AI giants could unravel if investors start demanding returns
The parallels between today’s AI surge and the dot-com bubble
What business leaders can do to stay grounded and make AI work for them
AI isn’t going anywhere, but the way we use it will define who thrives and who disappears when the hype fades.
👇 Let’s discuss: Do you think we’re in an AI bubble? What steps is your organization taking to ensure AI investments create real value?

An AWS outage took down apps around the world this week and it exposed a bigger question about the future of cloud, SaaS, and AI-first strategies.In this episode of Leading Change in the Wild, I break down what happened during the Virginia data center failure and what it signals for organizations that are pushing automation, AI decision-making, and cloud dependency deeper into critical infrastructure.Here is what I explore:
Cloud was supposed to make uptime safer, but automation took the system down
AI-first strategies are removing humans from the loop while infrastructure is getting more fragile
Enterprises are rethinking disaster recovery when everything runs in the cloud
Subscription fatigue is driving a return to building and hosting in-house
The pendulum may be swinging back from SaaS and cloud to proprietary and on-prem
My biggest question is this. If automation and cloud fail, what will still work when we have removed the human expertise that was used to manage the system?👇 I would love to hear your take:
- Are outages like this a warning that we bet too much on cloud and automation?
- Do you see companies starting to build in-house again instead of buying subscriptions?

Would you settle for a partial refund on a $440,000 report that used fake sources generated by AI?
That’s exactly what happened in what headlines are calling “The Blunder Down Under.” Deloitte reportedly delivered a government audit built on fabricated citations, and only after being caught did they admit the “sources” never existed.
In this episode of Leading Change in the Wild, Ema Roloff and Ian McCain unpack what this story reveals about the growing human intelligence problem behind our obsession with AI efficiency.
Here’s what they explore:- How Deloitte’s AI blunder exposed a lack of digital literacy at the highest levels- Why “AI-first” doesn’t mean “think later”- What real accountability looks like in an era of generative tools- Why leaders need to build critical thinking alongside AI fluency
This isn’t just about one consulting firm. It is a wake-up call for every organization chasing the next big tech trend without understanding the risks.
👇 Drop your thoughts in the comments:- Do you think AI tools are making us smarter or lazier?- How is your organization balancing speed with accountability?
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The headlines say AI is taking everyone’s jobs… but the data doesn’t agree.
In this episode of Leading Change in the Wild, I break down a new report from Yale’s Budget Lab that challenges everything we’ve been hearing about AI-driven disruption. 
Here’s what the data actually shows:- Generative AI is shifting jobs at the same pace as past tech waves like the internet and computers- AI adoption is slowing among large enterprises because ROI isn’t matching the hype- The biggest “disruption” is happening inside the tech industry itself, not everywhere else- We’re still nowhere near the workforce shifts of the 1940s and 1950s
So what should leaders do now?Invest in upskilling. Focus on digital literacy. And help your teams prepare for how AI will change their work — not if it will replace them.

Ema is joined by Nick to explore the evolving role of UX in today’s rapidly changing digital landscape. From the early days of Apple and Adobe Photoshop to the rise of user experience as a discipline, Nick shares insights on how technology, design, and human-centered thinking continue to shape innovation.

Your team is screaming for a different kind of leadership, and in this episode of Leading Change, I show you exactly what they’re saying.
Instead of breaking down headlines or research, I went straight to the comment section on a recent TikTok video and asked:👉 “If you could give unfiltered feedback to leadership during a major change… what would you say?”
The responses were clear, passionate, and brutally honest.
In this episode, we break down:
The 5 things your employees are begging you to do differently
Why the “AI-first” mindset is making people feel left behind
The real difference between training and change management
The simple leadership habits that build trust during transformation
These are the patterns I’m seeing across industries, and if you’re a leader trying to navigate digital transformation, process changes, or AI implementation… you can’t afford to ignore this feedback.

Ema is joined by Jason Schultz, Chief Innovation & Technology Officer for an AmLaw 200 firm, to explore what it really takes to drive organizational change.
He shares how to identify early adopters and "fast followers," the importance of unlearning old habits, and why small wins and strong storytelling are key to sustainable innovation.

We used to own things. Now we rent them , and in some cases that includes our ability to think.
In this episode of Leading Change, I unpack the subscriptionification of everything, from Netflix and workout apps to enterprise software and your favorite AI tools. What started with a $9.99 music plan has turned into monthly fees for cognition.
⚠️ I hit my limit this week after running into paywall after paywall just trying to do research.So we are talking about:
Why the rise of AI is accelerating subscription culture
How tools like ChatGPT and Claude are creating dependency 
The growing cost of access to knowledge and cognitive outsourcing
What we’re losing when we “rent” our workflows, our thoughts, and our time
And most importantly, I ask the question:
Are we okay with our brains being billed out monthly like software?
📌 Drop a comment —How many active subscriptions do you have right now?What’s one you can’t live without?And what are you ready to cancel in the name of taking back some ownership?